scholarly journals Emergent elasticity in the neural code for space

2018 ◽  
Vol 115 (50) ◽  
pp. E11798-E11806 ◽  
Author(s):  
Samuel A. Ocko ◽  
Kiah Hardcastle ◽  
Lisa M. Giocomo ◽  
Surya Ganguli
Keyword(s):  
Neural Circuit ◽  
Topological Defects ◽  
Grid Cell ◽  
Grid Cells ◽  
Motion Cues ◽  
Hebbian Plasticity ◽  
Attractor Model ◽  
Cell Firing ◽  
Path Dependent ◽  
Self Motion

Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including (i) systematic path-dependent shifts in the firing fields of grid cells toward the most recently encountered landmark, even in a fully learned environment; (ii) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers; and (iii) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.

scholarly journals Emergent Elasticity in the Neural Code for Space

2018 ◽  
Author(s):  
Samuel Ocko ◽  
Kiah Hardcastle ◽  
Lisa Giocomob ◽  
Surya Ganguli
Keyword(s):  
Neural Circuit ◽  
Topological Defects ◽  
Grid Cell ◽  
Grid Cells ◽  
Motion Cues ◽  
Hebbian Plasticity ◽  
Attractor Model ◽  
Cell Firing ◽  
Path Dependent ◽  
Self Motion

Upon encountering a novel environment, an animal must construct a consistent environmental map, as well as an internal estimate of its position within that map, by combining information from two distinct sources: self-motion cues and sensory landmark cues. How do known aspects of neural circuit dynamics and synaptic plasticity conspire to accomplish this feat? Here we show analytically how a neural attractor model that combines path integration of self-motion cues with Hebbian plasticity in synaptic weights from landmark cells can self-organize a consistent map of space as the animal explores an environment. Intriguingly, the emergence of this map can be understood as an elastic relaxation process between landmark cells mediated by the attractor network. Moreover, our model makes several experimentally testable predictions, including: (1) systematic path-dependent shifts in the firing field of grid cells towards the most recently encountered landmark, even in a fully learned environment, (2) systematic deformations in the firing fields of grid cells in irregular environments, akin to elastic deformations of solids forced into irregular containers, and (3) the creation of topological defects in grid cell firing patterns through specific environmental manipulations. Taken together, our results conceptually link known aspects of neurons and synapses to an emergent solution of a fundamental computational problem in navigation, while providing a unified account of disparate experimental observations.

scholarly journals Altered neural odometry in the vertical dimension

2019 ◽  
Vol 116 (10) ◽  
pp. 4631-4636 ◽  
Author(s):  
Giulio Casali ◽  
Daniel Bush ◽  
Kate Jeffery
Keyword(s):  
Vertical Plane ◽  
Grid Cell ◽  
Vertical Surface ◽  
The Body ◽  
Grid Cells ◽  
Grid Pattern ◽  
3D Space ◽  
Cell Firing ◽  
Circular Grid ◽  
Self Motion

Entorhinal grid cells integrate sensory and self-motion inputs to provide a spatial metric of a characteristic scale. One function of this metric may be to help localize the firing fields of hippocampal place cells during formation and use of the hippocampal spatial representation (“cognitive map”). Of theoretical importance is the question of how this metric, and the resulting map, is configured in 3D space. We find here that when the body plane is vertical as rats climb a wall, grid cells produce stable, almost-circular grid-cell firing fields. This contrasts with previous findings when the body was aligned horizontally during vertical exploration, suggesting a role for the body plane in orienting the plane of the grid cell map. However, in the present experiment, the fields on the wall were fewer and larger, suggesting an altered or absent odometric (distance-measuring) process. Several physiological indices of running speed in the entorhinal cortex showed reduced gain, which may explain the enlarged grid pattern. Hippocampal place fields were found to be sparser but unchanged in size/shape. Together, these observations suggest that the orientation and scale of the grid cell map, at least on a surface, are determined by an interaction between egocentric information (the body plane) and allocentric information (the gravity axis). This may be mediated by the different sensory or locomotor information available on a vertical surface and means that the resulting map has different properties on a vertical plane than a horizontal plane (i.e., is anisotropic).

scholarly journals Spatial Cognition: Grid Cell Firing Depends on Self-Motion Cues

2015 ◽  
Vol 25 (19) ◽  
pp. R827-R829 ◽  
Author(s):  
H. Freyja Ólafsdóttir ◽  
Caswell Barry
Keyword(s):  
Spatial Cognition ◽  
Grid Cell ◽  
Motion Cues ◽  
Cell Firing ◽  
Self Motion

scholarly journals Neurobiological successor features for spatial navigation

2019 ◽  
Author(s):  
William de Cothi ◽  
Caswell Barry
Keyword(s):  
Grid Cell ◽  
Biological Data ◽  
Basis Set ◽  
Grid Cells ◽  
Dimensional Representation ◽  
Boundary Vector ◽  
Good Account ◽  
Cell Firing ◽  
Low Dimensional ◽  
Environmental Geometry

AbstractThe hippocampus has long been observed to encode a representation of an animal’s position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modelled by learning a successor representation (SR) between distinct positions in an environment. However, this discretisation of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features – boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low-dimensional representation of these successor features. We show that the place and grid cells generated using the BVC-SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space.

scholarly journals Path integration in place cells of developing rats

2018 ◽  
Vol 115 (7) ◽  
pp. E1637-E1646 ◽  
Author(s):  
Tale L. Bjerknes ◽  
Nenitha C. Dagslott ◽  
Edvard I. Moser ◽  
May-Britt Moser
Keyword(s):  
Path Integration ◽  
Grid Cell ◽  
Cell System ◽  
Place Cells ◽  
Postnatal Life ◽  
Motion Information ◽  
Grid Cells ◽  
Adult Rats ◽  
Self Motion ◽  
Made In

Place cells in the hippocampus and grid cells in the medial entorhinal cortex rely on self-motion information and path integration for spatially confined firing. Place cells can be observed in young rats as soon as they leave their nest at around 2.5 wk of postnatal life. In contrast, the regularly spaced firing of grid cells develops only after weaning, during the fourth week. In the present study, we sought to determine whether place cells are able to integrate self-motion information before maturation of the grid-cell system. Place cells were recorded on a 200-cm linear track while preweaning, postweaning, and adult rats ran on successive trials from a start wall to a box at the end of a linear track. The position of the start wall was altered in the middle of the trial sequence. When recordings were made in complete darkness, place cells maintained fields at a fixed distance from the start wall regardless of the age of the animal. When lights were on, place fields were determined primarily by external landmarks, except at the very beginning of the track. This shift was observed in both young and adult animals. The results suggest that preweaning rats are able to calculate distances based on information from self-motion before the grid-cell system has matured to its full extent.

scholarly journals Differential influences of environment and self-motion on place and grid cell firing

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Guifen Chen ◽  
Yi Lu ◽  
John A King ◽  
Francesca Cacucci ◽  
Neil Burgess
Keyword(s):  
Grid Cell ◽  
Cell Firing ◽  
Self Motion

scholarly journals Irregular distribution of grid cell firing fields in rats exploring a 3D volumetric space

2021 ◽  
Author(s):  
Roddy M. Grieves ◽  
Selim Jedidi-Ayoub ◽  
Karyna Mishchanchuk ◽  
Anyi Liu ◽  
Sophie Renaudineau ◽  
...  
Keyword(s):  
Grid Cell ◽  
Lower Surface ◽  
Close Packing ◽  
Three Dimensions ◽  
Self Organization ◽  
Grid Cells ◽  
Spatial Regularity ◽  
Cell Firing ◽  
Temporal Firing ◽  
Firing Characteristics

AbstractWe investigated how entorhinal grid cells encode volumetric space. On a horizontal surface, grid cells usually produce multiple, spatially focal, approximately circular firing fields that are evenly sized and spaced to form a regular, close-packed, hexagonal array. This spatial regularity has been suggested to underlie navigational computations. In three dimensions, theoretically the equivalent firing pattern would be a regular, hexagonal close packing of evenly sized spherical fields. In the present study, we report that, in rats foraging in a cubic lattice, grid cells maintained normal temporal firing characteristics and produced spatially stable firing fields. However, although most grid fields were ellipsoid, they were sparser, larger, more variably sized and irregularly arranged, even when only fields abutting the lower surface (equivalent to the floor) were considered. Thus, grid self-organization is shaped by the environment’s structure and/or movement affordances, and grids may not need to be regular to support spatial computations.

scholarly journals Neural dynamics indicate parallel integration of environmental and self-motion information by place and grid cells

2019 ◽  
Author(s):  
Dmitri Laptev ◽  
Neil Burgess
Keyword(s):  
Path Integration ◽  
Temporal Dynamics ◽  
Hippocampal Formation ◽  
Place Cell ◽  
Place Cells ◽  
Motion Information ◽  
Grid Cells ◽  
Attractor Network ◽  
Cell Firing ◽  
Self Motion

AbstractPlace cells and grid cells in the hippocampal formation are thought to integrate sensory and self-motion information into a representation of estimated spatial location, but the precise mechanism is unknown. We simulated a parallel attractor system in which place cells form an attractor network driven by environmental inputs and grid cells form an attractor network performing path integration driven by self-motion, with inter-connections between them allowing both types of input to influence firing in both ensembles. We show that such a system is needed to explain the spatial patterns and temporal dynamics of place cell firing when rats run on a linear track in which the familiar correspondence between environmental and self-motion inputs is changed (Gothard et al., 1996b; Redish et al., 2000). In contrast, the alternative architecture of a single recurrent network of place cells (performing path integration and receiving environmental inputs) cannot reproduce the place cell firing dynamics. These results support the hypothesis that grid and place cells provide two different but complementary attractor representations (based on self-motion and environmental sensory inputs respectively). Our results also indicate the specific neural mechanism and main predictors of hippocampal map realignment and make predictions for future studies.

scholarly journals Hebbian Analysis of the Transformation of Medial Entorhinal Grid-Cell Inputs to Hippocampal Place Fields

2010 ◽  
Vol 103 (6) ◽  
pp. 3167-3183 ◽  
Author(s):  
Francesco Savelli ◽  
James J. Knierim
Keyword(s):  
Feedback Inhibition ◽  
Grid Cell ◽  
Nonlinear Process ◽  
Place Cells ◽  
Spike Timing ◽  
Perforant Path ◽  
Grid Cells ◽  
Novel Environment ◽  
Hebbian Plasticity ◽  
Place Fields

The discovery of grid cells in the medial entorhinal cortex (MEC) permits the characterization of hippocampal computation in much greater detail than previously possible. The present study addresses how an integrate-and-fire unit driven by grid-cell spike trains may transform the multipeaked, spatial firing pattern of grid cells into the single-peaked activity that is typical of hippocampal place cells. Previous studies have shown that in the absence of network interactions, this transformation can succeed only if the place cell receives inputs from grids with overlapping vertices at the location of the place cell's firing field. In our simulations, the selection of these inputs was accomplished by fast Hebbian plasticity alone. The resulting nonlinear process was acutely sensitive to small input variations. Simulations differing only in the exact spike timing of grid cells produced different field locations for the same place cells. Place fields became concentrated in areas that correlated with the initial trajectory of the animal; the introduction of feedback inhibitory cells reduced this bias. These results suggest distinct roles for plasticity of the perforant path synapses and for competition via feedback inhibition in the formation of place fields in a novel environment. Furthermore, they imply that variability in MEC spiking patterns or in the rat's trajectory is sufficient for generating a distinct population code in a novel environment and suggest that recalling this code in a familiar environment involves additional inputs and/or a different mode of operation of the network.

scholarly journals Independence of landmark and self-motion-guided navigation: a different role for grid cells

2014 ◽  
Vol 369 (1635) ◽  
pp. 20130370 ◽  
Author(s):  
Bruno Poucet ◽  
Francesca Sargolini ◽  
Eun Y. Song ◽  
Balázs Hangya ◽  
Steven Fox ◽  
...  
Keyword(s):  
Cell Activity ◽  
Place Cell ◽  
Grid Cells ◽  
Distance Information ◽  
Motion Cues ◽  
Neuronal Populations ◽  
System Changes ◽  
Navigational System ◽  
Self Motion ◽  
Contrary Evidence

Recent interest in the neural bases of spatial navigation stems from the discovery of neuronal populations with strong, specific spatial signals. The regular firing field arrays of medial entorhinal grid cells suggest that they may provide place cells with distance information extracted from the animal's self-motion, a notion we critically review by citing new contrary evidence. Next, we question the idea that grid cells provide a rigid distance metric. We also discuss evidence that normal navigation is possible using only landmarks, without self-motion signals. We then propose a model that supposes that information flow in the navigational system changes between light and dark conditions. We assume that the true map-like representation is hippocampal and argue that grid cells have a crucial navigational role only in the dark. In this view, their activity in the light is predominantly shaped by landmarks rather than self-motion information, and so follows place cell activity; in the dark, their activity is determined by self-motion cues and controls place cell activity. A corollary is that place cell activity in the light depends on non-grid cells in ventral medial entorhinal cortex. We conclude that analysing navigational system changes between landmark and no-landmark conditions will reveal key functional properties.

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